DEEP-SEA: Deep-Learning Enhancement for Environmental Perception in Submerged Aquatics

TL;DR

DEEP-SEA introduces a deep learning model that significantly improves underwater image quality by restoring details and structures, thereby enhancing ecological monitoring and autonomous exploration in aquatic environments.

Contribution

It presents a novel dual-frequency self-attention model that adaptively enhances underwater images, outperforming existing methods in detail restoration and structural preservation.

Findings

Superior restoration of fine details and structures.

Effective mitigation of underwater visual degradation.

Enhanced reliability for ecological and navigational applications.

Abstract

Continuous and reliable underwater monitoring is essential for assessing marine biodiversity, detecting ecological changes and supporting autonomous exploration in aquatic environments. Underwater monitoring platforms rely on mainly visual data for marine biodiversity analysis, ecological assessment and autonomous exploration. However, underwater environments present significant challenges due to light scattering, absorption and turbidity, which degrade image clarity and distort colour information, which makes accurate observation difficult. To address these challenges, we propose DEEP-SEA, a novel deep learning-based underwater image restoration model to enhance both low- and high-frequency information while preserving spatial structures. The proposed Dual-Frequency Enhanced Self-Attention Spatial and Frequency Modulator aims to adaptively refine feature representations in frequency domains and simultaneously spatial information for better structural preservation. Our comprehensive experiments on EUVP and LSUI datasets demonstrate the superiority over the state of the art in restoring fine-grained image detail and structural consistency. By effectively mitigating underwater visual degradation, DEEP-SEA has the potential to improve the reliability of underwater monitoring platforms for more accurate ecological observation, species identification and autonomous navigation.